Summary of Batch Ensemble For Variance Dependent Regret in Stochastic Bandits, by Asaf Cassel (1) et al.
Batch Ensemble for Variance Dependent Regret in Stochastic Bandits
by Asaf Cassel, Orin Levy, Yishay Mansour
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary As educators in machine learning, we explore a crucial challenge in online Reinforcement Learning (RL): efficiently trading off exploration and exploitation. Most approaches rely on estimating model uncertainty, adopting the optimistic model strategy. This work proposes a novel batch ensemble scheme that achieves near-optimal regret for stochastic Multi-Armed Bandits (MAB). Notably, our algorithm has only one adjustable parameter: the number of batches, which doesn’t depend on distributional properties. We substantiate this with theoretical results and synthetic benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a balance between trying new things and sticking to what works in online learning. The goal is to make good decisions when you don’t know what will happen next. Most people solve this problem by guessing how sure they are about different choices, then choosing the best one. This work takes a different approach, using a special kind of team (called an ensemble) to decide which options to try. This team only needs one setting changed, and it doesn’t matter if the outcomes are big or small. The results show that this new method is really effective. |
Keywords
» Artificial intelligence » Machine learning » Online learning » Reinforcement learning
